David Looney

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A framework for the robust assessment of phase synchrony between multichannel observations is introduced. This is achieved by using Empirical Mode Decomposition (EMD), a data driven technique which decomposes nonlinear and nonstationary data into their oscillatory components (scales). In general, it is rarely possible to jointly process two or more channels(More)
Empirical mode decomposition (EMD) is a fully data driven technique for decomposing signals into their natural scale components. However the problem of uniqueness, caused by the empirical nature of the algorithm and its sensitivity to changes in parameters, makes it difficult to perform fusion of data from multiple and heterogeneous sources. A solution to(More)
The integration of brain monitoring based on electroencephalography (EEG) into everyday life has been hindered by the limited portability and long setup time of current wearable systems as well as by the invasiveness of implanted systems (e.g. intracranial EEG). We explore the potential to record EEG in the ear canal, leading to a discreet, unobtrusive, and(More)
Brain electrical activity recorded via electroencephalogram (EEG) is the most convenient means for brain-computer interface (BCI), and is notoriously noisy. The information of interest is located in well defined frequency bands, and a number of standard frequency estimation algorithms have been used for feature extraction. To deal with data nonstationarity,(More)
A novel method is introduced to determine asymmetry, the lateralization of brain activity, using extension of the algorithm empirical mode decomposition (EMD). The localized and adaptive nature of EMD make it highly suitable for estimating amplitude information across frequency for nonlinear and nonstationary data. Analysis illustrates how bivariate(More)
Empirical mode decomposition (EMD) is a fully data driven technique for decomposing signals into their natural scale components. Given its ability to separate spatial frequencies, it is natural to consider EMD for image fusion. However the problem of uniqueness, caused by the empirical nature of the algorithm and its sensitivity to parameters, makes it(More)
A data-adaptive algorithm for the entropy-based analysis of structural regularities (complexity) in multivariate signals is proposed. This is achieved by combining multivariate sample entropy with a multivariate extension of empirical mode decomposition, both data-driven multiscale techniques. The proposed analysis across data-adaptive scales makes the(More)
Electroencephalogram (EEG) provides a non-invasive way to analyze brain activity. Blinking and movement of the eyes causes a strong electrical activity that can contaminate EEG recordings, particularly around the forehead but also as far as in occipital areas. Removal of such ocular artifacts is a considerable signal processing problem, since those(More)
A method for brain monitoring based on measuring the electroencephalogram (EEG) from electrodes placed in-the-ear (ear-EEG) was recently proposed. The objective of this study is to further characterize the ear-EEG and perform a rigorous comparison against conventional on-scalp EEG. This is achieved for both auditory and visual evoked responses, over(More)
A method for brain monitoring based on measuring electroencephalographic (EEG) signals from electrodes placed in-the-ear (Ear-EEG) was recently proposed. The Ear-EEG recording methodology provides a non-invasive, discreet and unobtrusive way of measuring electrical brain signals and has great potential as an enabling method for brain monitoring in everyday(More)